The public nuisance and health concerns caused by odours from livestock facilities are among the key issues that affect neighbouring communities and the growth of the livestock industry across Canada. A setback distance is the common regulatory practice to reduce odour impact on the neighbouring areas. The air dispersion modeling method may be a more accurate tool for establishing setback distances since it considers site-specific airborne emissions, such as odour and gases from the animal production site as well as weather conditions and then estimates a concentration of the pollutant (odour, ammonia, etc.). Although various dispersion models have been studied to predict odour concentration from agricultural sources, limited field data exist to evaluate their applicability in agricultural odour dispersion. Thus, the purpose of this project was to evaluate the selected commercial air dispersion models with field plume measurements from swine operations. <p>Firstly, this thesis describes a sensitivity analysis of how the climatic parameters affect model simulations for four selected air dispersion models, ISCST3, AUSPLUME, CALPUFF, and CALPUFF. Under the steady state weather condition, mixing height had no effect on the livestock odour dispersion, while atmospheric stability, wind speed and wind direction had great effect on the livestock odour dispersion. Ambient temperature had a moderate effect compared with other parameters. Under variable weather conditions, the predicted odour concentrations were much lower than the results under steady state weather conditions. <p>A series of comparisons between model predictions of the same four models and field odour measurements were conducted. When using the livestock odour plume measurement data from University of Manitoba, three equations were used to convert the model predicted odour concentration to field measured odour intensity. The equations did not predict odour intensity very well. No model showed obvious better performance than the others. Scaling factors did not improve the results considerably. When using the odour plume measurement data from University of Minnesota, INPUFF2 performed better than CALPUFF. Scaling factors did improve the modeled results. When using the odour plume measurement data from University of Saskatchewan, INPUFF2 also performed better than CALPUFF. Scaling factors were still useful for the results improvements.<p>Finally, because CALPUFF is the US EPA preferred model and predicted the highest values under variable weather conditions in the sensitivity study, we used it to simulate odour plumes on selected three swine sites using hourly weather data from 1993 to 2002 in Yorkton, Saskatchewan. The maximum predicted distance were 2.9 km for 1 OU, which was lower than the recommended maximum setback distance of 3.2 km. <p>It is recommended that the variable weather conditions be used in the setback distance determination. CALPUFF is the preferred model and INPUFF2 is another option for field odour plume simulation, however scaling factors are needed to bring the model predictions close to the field measured results. Because the models evaluated were not developed for odour dispersion simulation, a model that can accurately predict livestock odour dispersion should be developed to take into account of the difference between odour and gas and wind direction shifts within the simulation time interval.

The public nuisance and health concerns caused by odours from livestock facilities are among the key issues that affect neighbouring communities and the growth of the livestock industry across Canada. A setback distance is the common regulatory practice to reduce odour impact on the neighbouring areas. The air dispersion modeling method may be a more accurate tool for establishing setback distances since it considers site-specific airborne emissions, such as odour and gases from the animal production site as well as weather conditions and then estimates a concentration of the pollutant (odour, ammonia, etc.). Although various dispersion models have been studied to predict odour concentration from agricultural sources, limited field data exist to evaluate their applicability in agricultural odour dispersion. Thus, the purpose of this project was to evaluate the selected commercial air dispersion models with field plume measurements from swine operations. <p>Firstly, this thesis describes a sensitivity analysis of how the climatic parameters affect model simulations for four selected air dispersion models, ISCST3, AUSPLUME, CALPUFF, and CALPUFF. Under the steady state weather condition, mixing height had no effect on the livestock odour dispersion, while atmospheric stability, wind speed and wind direction had great effect on the livestock odour dispersion. Ambient temperature had a moderate effect compared with other parameters. Under variable weather conditions, the predicted odour concentrations were much lower than the results under steady state weather conditions. <p>A series of comparisons between model predictions of the same four models and field odour measurements were conducted. When using the livestock odour plume measurement data from University of Manitoba, three equations were used to convert the model predicted odour concentration to field measured odour intensity. The equations did not predict odour intensity very well. No model showed obvious better performance than the others. Scaling factors did not improve the results considerably. When using the odour plume measurement data from University of Minnesota, INPUFF2 performed better than CALPUFF. Scaling factors did improve the modeled results. When using the odour plume measurement data from University of Saskatchewan, INPUFF2 also performed better than CALPUFF. Scaling factors were still useful for the results improvements.<p>Finally, because CALPUFF is the US EPA preferred model and predicted the highest values under variable weather conditions in the sensitivity study, we used it to simulate odour plumes on selected three swine sites using hourly weather data from 1993 to 2002 in Yorkton, Saskatchewan. The maximum predicted distance were 2.9 km for 1 OU, which was lower than the recommended maximum setback distance of 3.2 km. <p>It is recommended that the variable weather conditions be used in the setback distance determination. CALPUFF is the preferred model and INPUFF2 is another option for field odour plume simulation, however scaling factors are needed to bring the model predictions close to the field measured results. Because the models evaluated were not developed for odour dispersion simulation, a model that can accurately predict livestock odour dispersion should be developed to take into account of the difference between odour and gas and wind direction shifts within the simulation time interval.

Impact of odour emissions from livestock operation sites on the air quality of neighboring areas has raised public concerns. A practical means to solve this problem is to set adequate setback distance. Air dispersion modeling was proved to be a promising method in predicting proper odour setback distance. Although a lot of air dispersion models have been used to predict odour concentrations downwind agricultural odour sources, not so much information regarding the capability of these models in odour dispersion modeling simulation could be found because very limited field odour data are available to be applied to evaluate the modeling result. A main purpose of this project was evaluating AERMOD and CALPUFF air dispersion models for odour dispersion simulation using field odour data.<p> Before evaluating and calibrating AERMOD and CALPUFF, sensitivity analysis of these two models to five major climatic parameters, i.e., mixing height, ambient temperature, stability class, wind speed, and wind direction, was conducted under both steady-state and variable meteorological conditions. It was found under steady-state weather condition, stability class and wind speed had great impact on the odour dispersion; while, ambient temperature and wind direction had limited impact on it; and mixing height had no impact on the odour dispersion at all. Under variable weather condition, maximum odour travel distance with odour concentrations of 1, 2, 5 and 10 OU/m3 were examined using annual hourly meteorological data of year 2003 of the simulated area and the simulation result showed odour traveled longer distance under the prevailing wind direction.<p> Evaluation outcomes of these two models using field odour data from University of Minnesota and University of Alberta showed capability of these two models in odour dispersion simulation was close in terms of agreement of modeled and field measured odour occurrences. Using Minnesota odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 3.6% applying conversion equation from University of Minnesota and 3.1% applying conversion equation from University of Alberta between two models. However, if field odour intensity 0 was not considered in Minnesota measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 3.1% applying conversion equation from University of Minnesota and 1.6% applying conversion equation from University of Alberta between two models. Using Alberta odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 0.7% applying conversion equation from University of Alberta and 1.2% applying conversion equation from University of Minnesota between two models. However, if field odour intensity 0 was not considered in Alberta measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 0.4% applying conversion equation from University of Alberta and 0.7% applying conversion equation from University of Minnesota between two models. Application of scaling factors can improve agreement of modeled and measured odour intensities (including all field odour measurements and field odour measurements without intensity 0) when conversion equation from University of Minnesota was used.<p> Both models were used in determining odour setback distance based on their close performance in odour dispersion simulation. Application of two models in predicting odour setback distance using warm season (from May to October) historical annul hourly meteorological data (from 1999 to 2002) for a swine farm in Saskatchewan showed some differences existed between models predicted and Prairie Provinces odour control guidelines recommended setbacks. Accurately measured field odour data and development of an air dispersion model for agricultural odour dispersion simulation purpose as well as acceptable odour criteria could be considered in the future studies.

Impact of odour emissions from livestock operation sites on the air quality of neighboring areas has raised public concerns. A practical means to solve this problem is to set adequate setback distance. Air dispersion modeling was proved to be a promising method in predicting proper odour setback distance. Although a lot of air dispersion models have been used to predict odour concentrations downwind agricultural odour sources, not so much information regarding the capability of these models in odour dispersion modeling simulation could be found because very limited field odour data are available to be applied to evaluate the modeling result. A main purpose of this project was evaluating AERMOD and CALPUFF air dispersion models for odour dispersion simulation using field odour data.<p> Before evaluating and calibrating AERMOD and CALPUFF, sensitivity analysis of these two models to five major climatic parameters, i.e., mixing height, ambient temperature, stability class, wind speed, and wind direction, was conducted under both steady-state and variable meteorological conditions. It was found under steady-state weather condition, stability class and wind speed had great impact on the odour dispersion; while, ambient temperature and wind direction had limited impact on it; and mixing height had no impact on the odour dispersion at all. Under variable weather condition, maximum odour travel distance with odour concentrations of 1, 2, 5 and 10 OU/m3 were examined using annual hourly meteorological data of year 2003 of the simulated area and the simulation result showed odour traveled longer distance under the prevailing wind direction.<p> Evaluation outcomes of these two models using field odour data from University of Minnesota and University of Alberta showed capability of these two models in odour dispersion simulation was close in terms of agreement of modeled and field measured odour occurrences. Using Minnesota odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 3.6% applying conversion equation from University of Minnesota and 3.1% applying conversion equation from University of Alberta between two models. However, if field odour intensity 0 was not considered in Minnesota measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 3.1% applying conversion equation from University of Minnesota and 1.6% applying conversion equation from University of Alberta between two models. Using Alberta odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 0.7% applying conversion equation from University of Alberta and 1.2% applying conversion equation from University of Minnesota between two models. However, if field odour intensity 0 was not considered in Alberta measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 0.4% applying conversion equation from University of Alberta and 0.7% applying conversion equation from University of Minnesota between two models. Application of scaling factors can improve agreement of modeled and measured odour intensities (including all field odour measurements and field odour measurements without intensity 0) when conversion equation from University of Minnesota was used.<p> Both models were used in determining odour setback distance based on their close performance in odour dispersion simulation. Application of two models in predicting odour setback distance using warm season (from May to October) historical annul hourly meteorological data (from 1999 to 2002) for a swine farm in Saskatchewan showed some differences existed between models predicted and Prairie Provinces odour control guidelines recommended setbacks. Accurately measured field odour data and development of an air dispersion model for agricultural odour dispersion simulation purpose as well as acceptable odour criteria could be considered in the future studies.